The main objective of this work is to achieve a high degree of accuracy while customizing predictions to the distinct data distribution of each particular center in the field of deep learning-powered clinical decision-making. In order to accomplish this challenging goal, neural networks must be trained on small, internal datasets, which poses difficulties in achieving the requisite accuracy levels. Additionally, a significant barrier is the resource-intensive nature of training profoundly deep models for specific medical applications, such as cancer-grade classification or neurodegeneration classification.
To this end, the central thrust of this project extends beyond the confines of specific clinical tasks as it aims to forge a foundational model for the human brain, one that transcends the boundaries of narrow applications. This foundational model will be meticulously crafted to encapsulate essential features of brain-related data. The features extracted from this foundational model will, in turn, serve as a valuable resource for training personalized downstream models tailored to specific clinical tasks and individual datasets.
By adopting this innovative approach, this project will only streamline resource utilization but also usher in a new era of precision and personalization in clinical decision-making. Ultimately, this work stands to make a profound impact on public health, by enhancing the accuracy of medical diagnoses and treatment recommendations, and by empowering healthcare professionals with a powerful tool to improve patient outcomes.
The timeline for this project is 24 months.
